In today's information-driven world, access to scientific publications has become increasingly easy. At the same time, filtering through the massive volume of available research has become more challenging than ever. Graph Neural Networks (GNNs) and graph attention mechanisms have shown strong effectiveness in searching large-scale information databases, particularly when combined with modern large language models. In this paper, we propose an Attention-Based Subgraph Retriever, a GNN-as-retriever model that applies attention-based pruning to extract a refined subgraph, which is then passed to a large language model for advanced knowledge reasoning.

翻译：在当今信息驱动的世界中，科学文献的获取已变得日益便捷。与此同时，从海量可用研究中筛选信息却变得比以往更具挑战性。图神经网络（GNNs）与图注意力机制在搜索大规模信息数据库方面展现出强大效能，尤其在与现代大语言模型结合时表现突出。本文提出一种基于注意力的子图检索器，该模型采用GNN作为检索器，通过基于注意力的剪枝技术提取精炼子图，随后将其输入大语言模型进行高级知识推理。